UMAP
Uniform Manifold Approximation and Projection is a fast, highly scalable algorithm for dimensionality reduction that preserves global data structure well.
Think of It Like This
Like taking a 3D scan of a sprawling city and creating a highly accurate 2D paper map that preserves the relative distances of all the neighborhoods.
UMAP builds a high-dimensional graph of the data and then optimizes a low-dimensional graph to be as structurally similar as possible. Compared to t-SNE, UMAP is significantly faster and better at preserving the global distances between distinct clusters, making it the modern standard for visualizing massive embedding datasets.